| Full text | |
| Author(s): |
Carvalho, Sarah Negreiros de
[1, 2]
;
Vargas, Guilherme Vettorazzi
[3]
;
da Silva Costa, Thiago Bulhoes
[2, 3]
;
de Arruda Leite, Harlei Miguel
[1, 2]
;
Coradine, Luis
[4]
;
Boccato, Levy
[3]
;
Soriano, Diogo Coutinho
[2, 5]
;
Attux, Romis
[2, 3]
Total Authors: 8
|
| Affiliation: | [1] Univ Fed Ouro Preto, Inst Exact & Appl Sci, Ouro Preto - Brazil
[2] BRAINN, Brazilian Inst Neurosci & Neurotechnol, Campinas - Brazil
[3] Univ Estadual Campinas, UNICAMP, Sch Comp & Elect Engn, Campinas - Brazil
[4] Univ Fed Alagoas, Inst Comp, UFAL, Maceio, Alagoas - Brazil
[5] Fed Univ ABC, Engn Modeling & Appl Social Sci Ctr, Santo Andre, SP - Brazil
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING; v. 59, n. 5, p. 1133-1150, MAY 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Brain-computer interfaces (BCI) based on steady-state visually evoked potentials (SSVEP) have been increasingly used in different applications, ranging from entertainment to rehabilitation. Filtering techniques are crucial to detect the SSVEP response since they can increase the accuracy of the system. Here, we present an analysis of a space-time filter based on the Minimum Variance Distortionless Response (MVDR). We have compared the performance of a BCI-SSVEP using the MVDR filter to other classical approaches: Common Average Reference (CAR) and Canonical Correlation Analysis (CCA). Moreover, we combined the CAR and MVDR techniques, totalling four filtering scenarios. Feature extraction was performed using Welch periodogram, Fast Fourier transform, and CCA (as extractor) with one and two harmonics. Feature selection was performed by forward wrappers, and a linear classifier was employed for discrimination. The main analyses were carried out over a database of ten volunteers, considering two cases: four and six visual stimuli. The results show that the BCI-SSVEP using the MVDR filter achieves the best performance among the analysed scenarios. Interestingly, the system's accuracy using the MVDR filter is practically constant even when the number of visual stimuli was increased, whereas degradation was observed for the other techniques. (AU) | |
| FAPESP's process: | 19/09512-0 - Nonlinear dynamic functional connectivity analysis via recurrence quantification and its application to brain computer-interfaces |
| Grantee: | Diogo Coutinho Soriano |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology |
| Grantee: | Fernando Cendes |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |